Estimation and Prediction of Individual Benefit From Postoperative Adjuvant Transcatheter Arterial Chemoembolization For Patients With Hepatocellular Carcinoma

Adjuvant transarterial chemoembolization (TACE) is widely adopted in China for resectable hepatocellular carcinoma (HCC), yet its efficacy remains inconsistent. We aimed to identify factors influencing individual patient benefit using causal machine learning. To this end, we retrospectively collected HCC patients with high risk factors for tumor recurrence from four centers of China, divided into the discovery cohort and the validation cohort . The primary endpoint was disease-free survival (DFS). The primary endpoint was overall survival (OS).Individual treatment effects (ITEs) were estimated within a causal machine learning framework. An ITE \< 0 was considered recommendation for adjuvant TACE , while ITE ≥ 0 indicated active surveillance. The model would be validated in the validation cohort. The contribution of each variable to ITE was assessed using the Shapley Additive Explanations (SHAP). An online calculator would be developed for future use by public.

NCT Number	NCT07562763
Lead Sponsor	Anhui Provincial Hospital Collaborators: The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Sun Yat-Sen University Cancer Center, No.2 People's Hospital of Fuyang City, Affiliated Hospital of Qinghai University, Anhui Provincial Cancer Hospital
Conditions	Hepatocellular Carcinoma (HCC), Transarterial Chemoembolization, Adjuvant Therapy, Machine Learning, Treatment Effectiveness
Enrollment	1,005 participants
Start Date	2018-01-01
Primary Completion	2025-12-31 (estimated)
Study Completion	2025-12-31 (estimated)
Updated on ClinicalTrials.gov	2026-07-09